Ovarian Cancer Screening and AI
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: ChatGPT - Control, ChatGPT - Evidence-Based Screening Discussion.
- Who it may be relevant to
- Registry conditions: Ovarian Cancer Screening Recommendations by Gynecologists. Basic parameters: from 24 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Germany
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
AI on Ovarian Cancer Screening Attitudes in Gynecologists
Overview
Gynecologists frequently overestimate the benefits and safety of ovarian cancer screening. AI-supported discussions may help correct these misperceptions. This study tests whether an AI-guided conversation about the evidence on ovarian cancer screening can improve gynecologists' knowledge and reduce non-evidence-based screening recommendations, compared with a control AI discussion on ovarian cancer prevalence.
Detailed description
Previous research has demonstrated that gynecologists often substantially overestimate both the effectiveness and safety of ovarian cancer screening, despite robust evidence indicating that such screening does not offer a net clinical benefit. These findings highlight the need for innovative communication strategies to support evidence-based clinical practice and reduce low value care.
AI-based conversational interventions have shown promising results in other fields when aiming to correct misconceptions or encourage engagement with evidence, particularly among individuals who are initially resistant to factual information. Leveraging these insights, this study investigates whether AI-facilitated discussions can effectively improve gynecologists' knowledge of the benefit-harm profile of ovarian cancer screening and subsequently reduce non-evidence-based recommendations.
The study employs a cross-sectional study design in which gynecologists who have previously indicated to regularly recommend ovarian cancer screening with transvaginal ultrasound and potentially with additional CA 125-testing to their asymptomatic, average-risk patients are randomized to one of two conditions:
1. Intervention Condition: Participants engage in an AI-guided conversation in which they explain their reasons for recommending ovarian cancer screening. The AI is instructed to address misconceptions and clarify the lack of evidence supporting a positive benefit-harm ratio. 2. Control Condition: Participants engage in an AI discussion on the prevalence of ovarian cancer, without receiving information or corrective feedback related to screening outcomes.
Before and after the AI-based discussion, all participants are queried on their numerical (X out of 1,000 women) and subjective perception of ovarian cancer screening's benefits and harms and their screening recommendations. Measures are derived from instruments used in prior research.
The primary objective of this study is to assess the change, from before to after the AI-based conversation, in clinicians' understanding of the benefit-harm ratio and their recommendations regarding routine ovarian cancer screening for asymptomatic, average-risk women, within and between study groups.
Interventions
- Behavioral ChatGPT - Control
Three-turn conversation; discusses ovarian cancer risk and epidemiology; avoids screening topics; concise responses (5-8 sentences). Mode of Delivery: Online chat interface; participant interacts directly with ChatGPT. - Behavioral ChatGPT - Evidence-Based Screening Discussion
Three-turn conversation; asks participants about screening rationale; provides evidence-based info on benefits/harms, trial data, guideline positions; concise responses (5-8 sentences). Mode of Delivery: Online chat interface; participant interacts directly with ChatGPT.
Primary outcome measures
- Change in intention to recommend ovarian cancer screening [Time frame: Immediately post intervention]
Secondary outcome measures (2)
- Change in benefit-harm ratio evaluation of ovarian cancer screenings [Time frame: Immediately post intervention]
- Accuracy of knowledge regarding ovarian cancer screening evidence [Time frame: Immediately post intervention]
Eligibility criteria
Inclusion criteria
- gynecologists in outpatient care who provide ovarian cancer screening to asymptomatic, average-risk women (not guideline consistent)
Exclusion criteria
- gynecologists in inpatient care
- gynecologist in outpatient care who do NOT provide ovarian cancer screening to asymptomatic, average-risk women (guideline consistent)
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Single blind
- Primary purpose
- Screening
Study locations
Germany · 1 center
- Charité - Universitätsmedizin Berlin — Mitte
Publications
- Wegwarth O, Gigerenzer G. US gynecologists' estimates and beliefs regarding ovarian cancer screening's effectiveness 5 years after release of the PLCO evidence. Sci Rep. 2018 Nov 21;8(1):17181. doi: 10.1038/s41598-018-35585-z. PMID 30464251
- Wegwarth O, Pashayan N. When evidence says no: gynaecologists' reasons for (not) recommending ineffective ovarian cancer screening. BMJ Qual Saf. 2020 Jun;29(6):521-524. doi: 10.1136/bmjqs-2019-009854. Epub 2019 Nov 8. No abstract available. PMID 31704891
- US Preventive Services Task Force; Grossman DC, Curry SJ, Owens DK, Barry MJ, Davidson KW, Doubeni CA, Epling JW Jr, Kemper AR, Krist AH, Kurth AE, Landefeld CS, Mangione CM, Phipps MG, Silverstein M, Simon MA, Tseng CW. Screening for Ovarian Cancer: US Preventive Services Task Force Recommendation Statement. JAMA. 2018 Feb 13;319(6):588-594. doi: 10.1001/jama.2017.21926. PMID 29450531
Identifiers
NCT: NCT07503054 · 2025ChatGPTGyn